Hi, I meet some problems when reproduce the results using pascal voc dataset

Hi, I try to reproduce your results reported in your paper but can’t reach your results as your paper report. Because of computation resource limited, I use batch size 8 and learning rate 0.0005 which are half in your paper. When try to reproduce “full” method using the superparameters mentioned before，I just reach 77.12 (running 3 times and average). Could you give me some advice to reproduce your method, thanks. The config file I used in reproduce experiment is as following. Besides, the annotations files are get as you mentioned in your repo.

dataset: # Required.
  type: pascal_semi
  train:
    data_root: xxx/VOCdevkit/VOC2012
    data_list: xxx/U2PL/data/splits/pascal/1464/labeled.txt
    flip: True
    GaussianBlur: False
    rand_resize: [0.5, 2.0]
    #rand_rotation: [-10.0, 10.0]
    crop:
      type: rand
      size: [513, 513] # crop image with HxW size
  val:
    data_root: xxx/VOCdevkit/VOC2012
    data_list: xxx/U2PL/data/splits/pascal/val.txt
    crop:
      type: center
      size: [513, 513] # crop image with HxW size
  batch_size: 2
  n_sup: 1464
  noise_std: 0.1
  workers: 2
  mean: [123.675, 116.28, 103.53]
  std: [58.395, 57.12, 57.375]
  ignore_label: 255

trainer: # Required.
  epochs: 80
  eval_on: True
  optimizer:
    type: SGD
    kwargs:
      lr: 0.0005  # 4GPUs
      momentum: 0.9
      weight_decay: 0.0001
  lr_scheduler:
    mode: poly
    kwargs:
      power: 0.9
  unsupervised:
    TTA: False
    drop_percent: 80
    apply_aug: cutmix
  contrastive:
    negative_high_entropy: True
    low_rank: 3
    high_rank: 20
    current_class_threshold: 0.3
    current_class_negative_threshold: 1
    unsupervised_entropy_ignore: 80
    low_entropy_threshold: 20
    num_negatives: 50
    num_queries: 256
    temperature: 0.5

saver:
  snapshot_dir: checkpoints
  pretrain: ''

criterion:
  type: CELoss
  kwargs:
    use_weight: False

net: # Required.
  num_classes: 21
  sync_bn: True
  ema_decay: 0.99
  encoder:
    type: u2pl.models.resnet.resnet101
    kwargs:
      multi_grid: True
      zero_init_residual: True
      fpn: True
      replace_stride_with_dilation: [False, True, True]  #layer0...1 is fixed, layer2...4
  decoder:
    type: u2pl.models.decoder.dec_deeplabv3_plus
    kwargs:
      inner_planes: 256
      dilations: [12, 24, 36]

[YanFangCS]

I have reproduced the result as paper declares. I solve this problem by using batch size 16, lr 0.001 with torch.cuda.amp, which is similar to apex. It consumes about 15G cuda memory for RTX3090 with amp which is affordable. So, I think batch size and lr are essential for reproducing this paper. The model can’t be sucessfully trained with half bs and lr, it still confuses me a lot. Thanks for your help.

[Haochen-Wang409]

Yes, an epoch is defined as the iterations that the model is trained by all unsupervised images.